IJMTES – EXPONENTIAL CONTRAST RESTORATION IN FOG CONDITIONS FOR DRIVING ASSISTANCE

Journal Title : International Journal of Modern Trends in Engineering and Science

Paper Title : EXPONENTIAL CONTRAST RESTORATION IN FOG CONDITIONS FOR DRIVING ASSISTANCE

Author’s Name : M.Nandha Kumar  unnamed

Volume 03 Issue 10 2016

ISSN no:  2348-3121

Page no: 108-109

Abstract – Images of outdoor scenes captured in bad weather suffer from poor contrast. Under bad weather conditions, the light reaching a camera is severely scattered by the atmosphere. So the image is getting highly degraded due to additive light. Additive light is created by mixing the visible light that is emitted from different light source. This additive light is called air light. Air light is not uniformly distributed in the image. Bad weather reduces atmospheric visibility. Poor visibility degrades perceptual image quality and performance of the computer vision algorithms such as surveillance, tracking and navigation. From the atmospheric point of view, weather conditions differ mainly in the types and sizes of the particles present in the space. A great effort has taken for measuring the size of these particles. Here the effective is method is used to restore degraded images based on an original mathematical model, for computing the atmospheric veil, taking into account the variation in haze density to the distance.   

Keywords— Driving Assistance; Restoration; Image Processing

Reference

  1. Hong Ren Wu and Stefan Winkler “Vision-Model-Based Impairment Metric to Evaluate Blocking Artifacts in Digital Video” IEEE 2002.
  2. Srinivasa G. Narasimhan and Shree K. Nayar “Removing Weather Effects from Monochrome Images” IEEE Vol 11,Vol 4, 2001.
  3. Harini Veeraraghavan, Osama Masoud and Nikolaos Papanikolopoulos “Real-Time Tracking for Managing Suburban Intersections” 3/02/02002 IEEE.
  4. Gouchol Pok, Jyh-Charn Liu, and Attoor Sanju Nair “Selective Removal of Impulse Noise Based on Homogeneity Level Information” IEEE transactions on image processing, vol. 12, no. 1, january 2003.
  5. Daniel Scharstein, Richard Szeliski and Ramin Zabih, Deptartment of Math and Computer Science “A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithm” Nov. 2009.
  6. Stan Birchfield, Department of Electrical Engineering, Stanford University “Depth Discontinuities by Pixel-to-Pixel Stereo” in NSF Graduate Student Fellowship, and by a gift from the Charles Lee Powell Foundation.
  7. Matting Anat, Levin Dani and Lischinski Yair “A Closed Form Solution to Natural Image” Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.